Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation
International Journal of Computer Vision
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
An Improved Algorithm for TV-L1 Optical Flow
Statistical and Geometrical Approaches to Visual Motion Analysis
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A dataset and evaluation methodology for template-based tracking algorithms
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Direct Estimation of Nonrigid Registrations with Image-Based Self-Occlusion Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Technical Section: Realistic cloth augmentation in single view video under occlusions
Computers and Graphics
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In this paper, we address the problem of simultaneous tracking and reconstruction of non-planar templates in real-time. Classical approaches to template tracking assume planarity and do not attempt to recover the shape of an object. Structure from motion approaches use feature points to recover camera pose and reconstruct the scene from those features, but do not produce dense 3D surface models. Finally, deformable surface tracking approaches assume a static camera and impose strong deformation priors to recover dense 3D shapes. The proposed method simultaneously recovers the camera motion and deforms the template such that an approximation of the underlying 3D structure is recovered. Spatial smoothing is not explicitly imposed, thus templates of smooth and non-smooth objects can be equally handled. The problem is formalized as an energy minimization based on image intensity differences. Quantitative and qualitative evaluation on both real and synthetic data is presented, we compare the proposed approach to related methods and demonstrate that the recovered camera pose is close to the ground truth even in presence of strong blur and low texture.